Accurately count GPT model tokens, compare models, estimate costs
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Multi-Model Token Comparison
| Model | Tokens | Input Cost | Output Cost | Context Window |
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💰 Cost Estimator
OpenAI Token Counter - Accurately Count GPT Tokens
The OpenAI Token Counter is a free online tool that helps developers accurately count the number of tokens in text for GPT models. Whether you're building ChatGPT applications, optimizing prompt lengths, or estimating API call costs, this tool provides accurate token counts.
What is a Token?
A Token is the basic unit that Large Language Models (LLMs) use to process text. OpenAI's models use the BPE (Byte Pair Encoding) tokenization algorithm to split text into tokens. In English text, 1 token is approximately 4 characters or 0.75 words. In Chinese text, 1 token is approximately 1-2 characters. Different models use different tokenizers, so the same text may have different token counts across models.
Supported Models & Tokenizers
- GPT-4o / o1 / o3-mini: Uses o200k_base tokenizer, more efficient for multilingual text
- GPT-4 / GPT-4 Turbo / GPT-3.5 Turbo: Uses cl100k_base tokenizer
- Claude 3.5 Sonnet: Anthropic's tokenization method
- Gemini 1.5 Pro: Google's tokenization method
Core features of OpenAI Token Counter
- Real-time Counting: Count as you type, no button clicks needed
- Multi-Model Comparison: See token counts across all models at once
- Cost Estimation: Auto-calculate API costs based on official model pricing
- Batch Analysis: Per-line token statistics for multi-line text
- Privacy Protection: Pure frontend processing, no data uploads
How to Optimize Token Usage
Reducing token consumption can significantly lower API costs. Here are some optimization tips:
- Use concise prompts, avoid redundant descriptions
- English is more token-efficient than Chinese for system prompts
- GPT-4o's o200k_base tokenizer is more efficient than cl100k_base
- Set max_tokens appropriately to avoid excessive output generation
- For simple tasks, use GPT-3.5 Turbo or GPT-4o mini instead of GPT-4
Token Counting Methodology
This tool implements the core logic of the BPE tokenization algorithm. For English text, it performs accurate character-level BPE tokenization. For Chinese text, it estimates based on Unicode encoding ranges and common tokenization rules. Accuracy: English 99%+, Chinese 95-98%. For 100% precise counting, we recommend using OpenAI's official tiktoken Python library.
When to use OpenAI Token Counter
- Estimate token consumption when developing ChatGPT plugins and applications
- Optimize prompt length to avoid exceeding context window limits
- Budget API call costs and plan monthly spending
- Compare token efficiency across different models
- Batch evaluate document token consumption